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  1. Article ; Online: The Use of Reframing: Increasing the Importance of Lifestyle Medicine.

    Vega, Molly R / Nadeem, Saad / Vaughan, Elizabeth M / Johnston, Craig A

    American journal of lifestyle medicine

    2023  Volume 17, Issue 6, Page(s) 746–749

    Abstract: Lifestyle behavior modification is an essential component to prevention and treatment of non-communicable diseases worldwide. For the last 40 years, studies have recognized that there is suboptimal training of physicians in lifestyle medicine and its ... ...

    Abstract Lifestyle behavior modification is an essential component to prevention and treatment of non-communicable diseases worldwide. For the last 40 years, studies have recognized that there is suboptimal training of physicians in lifestyle medicine and its implementation in clinical settings. The lack of nutrition and exercise counseling occurring in the medical office does not reflect the high level of evidence supporting its use. Lifestyle behavior counseling is complex; as are the individualized needs of patients. Therefore, we suspect that the lack of knowledge in nutrition and exercise prescriptions are not the only barriers to providing optimal care. Reframing lifestyle medicine interventions like nutrition and exercise from adjunctive to central to treatment and reframing the role of the physician therein may be necessary to address important barriers to overall lifestyle behavioral counseling.
    Language English
    Publishing date 2023-09-25
    Publishing country United States
    Document type Journal Article ; Review
    ZDB-ID 2265653-4
    ISSN 1559-8284 ; 1559-8276
    ISSN (online) 1559-8284
    ISSN 1559-8276
    DOI 10.1177/15598276231193643
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article: CLTS-GAN: Color-Lighting-Texture-Specular Reflection Augmentation for Colonoscopy.

    Mathew, Shawn / Nadeem, Saad / Kaufman, Arie

    Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention

    2022  Volume 2022, Page(s) 519–529

    Abstract: Automated analysis of optical colonoscopy (OC) video frames (to assist endoscopists during OC) is challenging due to variations in color, lighting, texture, and specular reflections. Previous methods either remove some of these variations via ... ...

    Abstract Automated analysis of optical colonoscopy (OC) video frames (to assist endoscopists during OC) is challenging due to variations in color, lighting, texture, and specular reflections. Previous methods either remove some of these variations via preprocessing (making pipelines cumbersome) or add diverse training data with annotations (but expensive and time-consuming). We present CLTS-GAN, a new deep learning model that gives fine control over color, lighting, texture, and specular reflection synthesis for OC video frames. We show that adding these colonoscopy-specific augmentations to the training data can improve state-of-the-art polyp detection/segmentation methods as well as drive next generation of OC simulators for training medical students. The code and pre-trained models for CLTS-GAN are available on Computational Endoscopy Platform GitHub (https://github.com/nadeemlab/CEP).
    Language English
    Publishing date 2022-09-17
    Publishing country Germany
    Document type Journal Article
    DOI 10.1007/978-3-031-16449-1_49
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article: CIRDataset: A large-scale Dataset for Clinically-Interpretable lung nodule Radiomics and malignancy prediction.

    Choi, Wookjin / Dahiya, Navdeep / Nadeem, Saad

    Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention

    2022  Volume 2022, Page(s) 13–22

    Abstract: Spiculations/lobulations, sharp/curved spikes on the surface of lung nodules, are good predictors of lung cancer malignancy and hence, are routinely assessed and reported by radiologists as part of the standardized Lung-RADS clinical scoring criteria. ... ...

    Abstract Spiculations/lobulations, sharp/curved spikes on the surface of lung nodules, are good predictors of lung cancer malignancy and hence, are routinely assessed and reported by radiologists as part of the standardized Lung-RADS clinical scoring criteria. Given the 3D geometry of the nodule and 2D slice-by-slice assessment by radiologists, manual spiculation/lobulation annotation is a tedious task and thus no public datasets exist to date for probing the importance of these clinically-reported features in the SOTA malignancy prediction algorithms. As part of this paper, we release a large-scale Clinically-Interpretable Radiomics Dataset, CIRDataset, containing 956 radiologist QA/QC'ed spiculation/lobulation annotations on segmented lung nodules from two public datasets, LIDC-IDRI (N=883) and LUNGx (N=73). We also present an end-to-end deep learning model based on multi-class Voxel2Mesh extension to segment nodules (while preserving spikes), classify spikes (sharp/spiculation and curved/lobulation), and perform malignancy prediction. Previous methods have performed malignancy prediction for LIDC and LUNGx datasets but without robust attribution to any clinically reported/actionable features (due to known hyperparameter sensitivity issues with general attribution schemes). With the release of this comprehensively-annotated CIRDataset and end-to-end deep learning baseline, we hope that malignancy prediction methods can validate their explanations, benchmark against our baseline, and provide clinically-actionable insights. Dataset, code, pretrained models, and docker containers are available at https://github.com/nadeemlab/CIR.
    Language English
    Publishing date 2022-09-16
    Publishing country Germany
    Document type Journal Article
    DOI 10.1007/978-3-031-16443-9_2
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article: DeepLIIF: An Online Platform for Quantification of Clinical Pathology Slides.

    Ghahremani, Parmida / Marino, Joseph / Dodds, Ricardo / Nadeem, Saad

    Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition

    2022  Volume 2022, Page(s) 21399–21405

    Abstract: In the clinic, resected tissue samples are stained with Hematoxylin-and-Eosin (H&E) and/or Immunhistochemistry (IHC) stains and presented to the pathologists on glass slides or as digital scans for diagnosis and assessment of disease progression. Cell- ... ...

    Abstract In the clinic, resected tissue samples are stained with Hematoxylin-and-Eosin (H&E) and/or Immunhistochemistry (IHC) stains and presented to the pathologists on glass slides or as digital scans for diagnosis and assessment of disease progression. Cell-level quantification, e.g. in IHC protein expression scoring, can be extremely inefficient and subjective. We present DeepLIIF (https://deepliif.org), a first free online platform for efficient and reproducible IHC scoring. DeepLIIF outperforms current state-of-the-art approaches (relying on manual error-prone annotations) by virtually restaining clinical IHC slides with more informative multiplex immunofluorescence staining. Our DeepLIIF cloud-native platform supports (1) more than 150 proprietary/non-proprietary input formats via the Bio-Formats standard, (2) interactive adjustment, visualization, and downloading of the IHC quantification results and the accompanying restained images, (3) consumption of an exposed workflow API programmatically or through interactive plugins for open source whole slide image viewers such as QuPath/ImageJ, and (4) auto scaling to efficiently scale GPU resources based on user demand.
    Language English
    Publishing date 2022-09-21
    Publishing country United States
    Document type Journal Article
    ISSN 1063-6919
    ISSN 1063-6919
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article: FoldIt: Haustral Folds Detection and Segmentation in Colonoscopy Videos.

    Mathew, Shawn / Nadeem, Saad / Kaufman, Arie

    Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention

    2021  Volume 12903, Page(s) 221–230

    Abstract: Haustral folds are colon wall protrusions implicated for high polyp miss rate during optical colonoscopy procedures. If segmented accurately, haustral folds can allow for better estimation of missed surface and can also serve as valuable landmarks for ... ...

    Abstract Haustral folds are colon wall protrusions implicated for high polyp miss rate during optical colonoscopy procedures. If segmented accurately, haustral folds can allow for better estimation of missed surface and can also serve as valuable landmarks for registering pre-treatment virtual (CT) and optical colonoscopies, to guide navigation towards the anomalies found in pre-treatment scans. We present a novel generative adversarial network, FoldIt, for feature-consistent image translation of optical colonoscopy videos to virtual colonoscopy renderings with haustral fold overlays. A new transitive loss is introduced in order to leverage ground truth information between haustral fold annotations and virtual colonoscopy renderings. We demonstrate the effectiveness of our model on real challenging optical colonoscopy videos as well as on textured virtual colonoscopy videos with clinician-verified haustral fold annotations. All code and scripts to reproduce the experiments of this paper will be made available via our Computational Endoscopy Platform at https://github.com/nadeemlab/CEP.
    Language English
    Publishing date 2021-09-21
    Publishing country Germany
    Document type Journal Article
    DOI 10.1007/978-3-030-87199-4_21
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article: VISUALIZING MISSING SURFACES IN COLONOSCOPY VIDEOS USING SHARED LATENT SPACE REPRESENTATIONS.

    Mathew, Shawn / Nadeem, Saad / Kaufman, Arie

    Proceedings. IEEE International Symposium on Biomedical Imaging

    2021  Volume 2021, Page(s) 329–333

    Abstract: Optical colonoscopy (OC), the most prevalent colon cancer screening tool, has a high miss rate due to a number of factors, including the geometry of the colon (haustral fold and sharp bends occlusions), endoscopist inexperience or fatigue, endoscope ... ...

    Abstract Optical colonoscopy (OC), the most prevalent colon cancer screening tool, has a high miss rate due to a number of factors, including the geometry of the colon (haustral fold and sharp bends occlusions), endoscopist inexperience or fatigue, endoscope field of view. We present a framework to visualize the missed regions per-frame during OC, and provides a workable clinical solution. Specifically, we make use of 3D reconstructed virtual colonoscopy (VC) data and the insight that VC and OC share the same underlying geometry but differ in color, texture and specular reflections, embedded in the OC. A lossy unpaired image-to-image translation model is introduced with enforced shared latent space for OC and VC. This shared space captures the geometric information while deferring the color, texture, and specular information creation to additional Gaussian noise input. The latter can be utilized to generate one-to-many mappings from VC to OC and OC to OC. The code, data and trained models will be released via our Computational Endoscopy Platform at https://github.com/nadeemlab/CEP.
    Language English
    Publishing date 2021-05-25
    Publishing country United States
    Document type Journal Article
    ISSN 1945-7928
    ISSN 1945-7928
    DOI 10.1109/isbi48211.2021.9433982
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: Domain knowledge driven 3D dose prediction using moment-based loss function.

    Jhanwar, Gourav / Dahiya, Navdeep / Ghahremani, Parmida / Zarepisheh, Masoud / Nadeem, Saad

    Physics in medicine and biology

    2022  Volume 67, Issue 18

    Abstract: Objective. ...

    Abstract Objective.
    MeSH term(s) Humans ; Neural Networks, Computer ; Organs at Risk ; Radiotherapy Dosage ; Radiotherapy Planning, Computer-Assisted/methods ; Radiotherapy, Intensity-Modulated/methods
    Language English
    Publishing date 2022-09-14
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 208857-5
    ISSN 1361-6560 ; 0031-9155
    ISSN (online) 1361-6560
    ISSN 0031-9155
    DOI 10.1088/1361-6560/ac8d45
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 199: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

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  8. Article ; Online: RMSim: controlled respiratory motion simulation on static patient scans.

    Lee, Donghoon / Yorke, Ellen / Zarepisheh, Masoud / Nadeem, Saad / Hu, Yu-Chi

    Physics in medicine and biology

    2023  Volume 68, Issue 4

    Abstract: Objective. ...

    Abstract Objective.
    MeSH term(s) Humans ; Image Processing, Computer-Assisted/methods ; Computer Simulation ; Four-Dimensional Computed Tomography/methods ; Algorithms ; Motion
    Language English
    Publishing date 2023-02-07
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 208857-5
    ISSN 1361-6560 ; 0031-9155
    ISSN (online) 1361-6560
    ISSN 0031-9155
    DOI 10.1088/1361-6560/acb484
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    In stock of ZB MED Cologne/Königswinter

    Zs.A 199: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

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  9. Article ; Online: Volume Exploration Using Multidimensional Bhattacharyya Flow.

    Jadhav, Shreeraj / Torkaman, Mahsa / Tannenbaum, Allen / Nadeem, Saad / Kaufman, Arie E

    IEEE transactions on visualization and computer graphics

    2023  Volume 29, Issue 3, Page(s) 1651–1663

    Abstract: We present a novel approach for volume exploration that is versatile yet effective in isolating semantic structures in both noisy and clean data. Specifically, we describe a hierarchical active contours approach based on Bhattacharyya gradient flow which ...

    Abstract We present a novel approach for volume exploration that is versatile yet effective in isolating semantic structures in both noisy and clean data. Specifically, we describe a hierarchical active contours approach based on Bhattacharyya gradient flow which is easier to control, robust to noise, and can incorporate various types of statistical information to drive an edge-agnostic exploration process. To facilitate a time-bound user-driven volume exploration process that is applicable to a wide variety of data sources, we present an efficient multi-GPU implementation that (1) is approximately 400 times faster than a single thread CPU implementation, (2) allows hierarchical exploration of 2D and 3D images, (3) supports customization through multidimensional attribute spaces, and (4) is applicable to a variety of data sources and semantic structures. The exploration system follows a 2-step process. It first applies active contours to isolate semantically meaningful subsets of the volume. It then applies transfer functions to the isolated regions locally to produce clear and clutter-free visualizations. We show the effectiveness of our approach in isolating and visualizing structures-of-interest without needing any specialized segmentation methods on a variety of data sources, including 3D optical microscopy, multi-channel optical volumes, abdominal and chest CT, micro-CT, MRI, simulation, and synthetic data. We also gathered feedback from a medical trainee regarding the usefulness of our approach and discussion on potential applications in clinical workflows.
    Language English
    Publishing date 2023-01-30
    Publishing country United States
    Document type Journal Article
    ISSN 1941-0506
    ISSN (online) 1941-0506
    DOI 10.1109/TVCG.2021.3127918
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Book ; Online: RT-GAN

    Mathew, Shawn / Nadeem, Saad / Goh, Alvin C. / Kaufman, Arie

    Recurrent Temporal GAN for Adding Lightweight Temporal Consistency to Frame-Based Domain Translation Approaches

    2023  

    Abstract: While developing new unsupervised domain translation methods for endoscopy videos, it is typical to start with approaches that initially work for individual frames without temporal consistency. Once an individual-frame model has been finalized, ... ...

    Abstract While developing new unsupervised domain translation methods for endoscopy videos, it is typical to start with approaches that initially work for individual frames without temporal consistency. Once an individual-frame model has been finalized, additional contiguous frames are added with a modified deep learning architecture to train a new model for temporal consistency. This transition to temporally-consistent deep learning models, however, requires significantly more computational and memory resources for training. In this paper, we present a lightweight solution with a tunable temporal parameter, RT-GAN (Recurrent Temporal GAN), for adding temporal consistency to individual frame-based approaches that reduces training requirements by a factor of 5. We demonstrate the effectiveness of our approach on two challenging use cases in colonoscopy: haustral fold segmentation (indicative of missed surface) and realistic colonoscopy simulator video generation. The datasets, accompanying code, and pretrained models will be made available at \url{https://github.com/nadeemlab/CEP}.

    Comment: First two authors contributed equally
    Keywords Computer Science - Computer Vision and Pattern Recognition ; Electrical Engineering and Systems Science - Image and Video Processing
    Subject code 004 ; 006
    Publishing date 2023-10-01
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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